Overview

Dataset statistics

Number of variables17
Number of observations30
Missing cells7
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory134.5 B

Variable types

DateTime1
Numeric14
Categorical2

Alerts

SS_BedTime is highly correlated with SS_wakeTime and 4 other fieldsHigh correlation
SS_wakeTime is highly correlated with SS_BedTime and 1 other fieldsHigh correlation
SS_Midpoint is highly correlated with SS_BedTime and 3 other fieldsHigh correlation
SS_SleepScore is highly correlated with SS_REMSleepDuration and 1 other fieldsHigh correlation
SS_REMSleepDuration is highly correlated with SS_SleepScoreHigh correlation
SS_LightSleepDuration is highly correlated with SS_BedTime and 2 other fieldsHigh correlation
SS_TotalSleepTime is highly correlated with SS_BedTime and 3 other fieldsHigh correlation
SS_Sleepiness is highly correlated with SS_BedTimeHigh correlation
SS_BedTime is highly correlated with SS_Midpoint and 3 other fieldsHigh correlation
SS_wakeTime is highly correlated with SS_MidpointHigh correlation
SS_Midpoint is highly correlated with SS_BedTime and 4 other fieldsHigh correlation
SS_SleepScore is highly correlated with SS_BedTime and 3 other fieldsHigh correlation
SS_REMSleepDuration is highly correlated with SS_SleepScoreHigh correlation
SS_LightSleepDuration is highly correlated with SS_BedTime and 2 other fieldsHigh correlation
SS_TotalSleepTime is highly correlated with SS_BedTime and 3 other fieldsHigh correlation
SS_BedTime is highly correlated with SS_Midpoint and 1 other fieldsHigh correlation
SS_wakeTime is highly correlated with SS_MidpointHigh correlation
SS_Midpoint is highly correlated with SS_BedTime and 1 other fieldsHigh correlation
SS_SleepScore is highly correlated with SS_REMSleepDuration and 1 other fieldsHigh correlation
SS_REMSleepDuration is highly correlated with SS_SleepScoreHigh correlation
SS_TotalSleepTime is highly correlated with SS_BedTime and 1 other fieldsHigh correlation
Date is highly correlated with SS_BedTime and 15 other fieldsHigh correlation
SS_BedTime is highly correlated with Date and 6 other fieldsHigh correlation
SS_wakeTime is highly correlated with DateHigh correlation
SS_Midpoint is highly correlated with Date and 5 other fieldsHigh correlation
SS_SleepScore is highly correlated with Date and 5 other fieldsHigh correlation
SS_DeepSleepDuration is highly correlated with Date and 4 other fieldsHigh correlation
SS_REMSleepDuration is highly correlated with Date and 4 other fieldsHigh correlation
SS_LightSleepDuration is highly correlated with Date and 4 other fieldsHigh correlation
SS_TotalSleepTime is highly correlated with Date and 5 other fieldsHigh correlation
SS_TimeToSleep is highly correlated with Date and 6 other fieldsHigh correlation
SS_WASO is highly correlated with Date and 5 other fieldsHigh correlation
SS_CaffeineCups is highly correlated with Date and 2 other fieldsHigh correlation
SS_AlcoholDrinks is highly correlated with Date and 3 other fieldsHigh correlation
SS_Stress is highly correlated with Date and 4 other fieldsHigh correlation
SS_Sleepiness is highly correlated with Date and 5 other fieldsHigh correlation
SS_Mood is highly correlated with Date and 1 other fieldsHigh correlation
Location is highly correlated with Date and 1 other fieldsHigh correlation
SS_Mood has 7 (23.3%) missing values Missing
Date has unique values Unique
SS_BedTime has unique values Unique
SS_wakeTime has unique values Unique
SS_Midpoint has unique values Unique
SS_TotalSleepTime has unique values Unique
SS_CaffeineCups has 26 (86.7%) zeros Zeros
SS_AlcoholDrinks has 26 (86.7%) zeros Zeros
SS_Stress has 2 (6.7%) zeros Zeros

Reproduction

Analysis started2022-11-23 08:14:22.790504
Analysis finished2022-11-23 08:14:58.121310
Duration35.33 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-10 00:00:00
2022-11-23T08:14:58.245967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:58.426136image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

SS_BedTime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.46846296
Minimum21.44611111
Maximum26.335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:14:58.626965image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21.44611111
5-th percentile22.22411111
Q122.68784722
median23.44277778
Q324.165
95-th percentile24.71840278
Maximum26.335
Range4.88888889
Interquartile range (IQR)1.477152783

Descriptive statistics

Standard deviation1.019986806
Coefficient of variation (CV)0.04346201996
Kurtosis0.7850539701
Mean23.46846296
Median Absolute Deviation (MAD)0.784722225
Skewness0.4940958927
Sum704.0538889
Variance1.040373084
MonotonicityNot monotonic
2022-11-23T08:14:58.819969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22.387222221
 
3.3%
22.571666671
 
3.3%
23.836944441
 
3.3%
24.241666671
 
3.3%
22.672222221
 
3.3%
23.275833331
 
3.3%
24.673333331
 
3.3%
24.308888891
 
3.3%
24.493888891
 
3.3%
23.64751
 
3.3%
Other values (20)20
66.7%
ValueCountFrequency (%)
21.446111111
3.3%
22.178611111
3.3%
22.279722221
3.3%
22.298888891
3.3%
22.337222221
3.3%
22.387222221
3.3%
22.571666671
3.3%
22.672222221
3.3%
22.734722221
3.3%
22.961388891
3.3%
ValueCountFrequency (%)
26.3351
3.3%
24.755277781
3.3%
24.673333331
3.3%
24.628611111
3.3%
24.50751
3.3%
24.493888891
3.3%
24.308888891
3.3%
24.241666671
3.3%
23.9351
3.3%
23.836944441
3.3%

SS_wakeTime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.44762963
Minimum6.884722222
Maximum8.376666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:14:59.106273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.884722222
5-th percentile6.958916667
Q17.171458334
median7.312083333
Q37.643125
95-th percentile8.218527778
Maximum8.376666667
Range1.491944445
Interquartile range (IQR)0.4716666668

Descriptive statistics

Standard deviation0.3940885607
Coefficient of variation (CV)0.05291462926
Kurtosis0.09587715937
Mean7.44762963
Median Absolute Deviation (MAD)0.192916667
Skewness0.9132653232
Sum223.4288889
Variance0.1553057937
MonotonicityNot monotonic
2022-11-23T08:14:59.289455image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7.3122222221
 
3.3%
6.981
 
3.3%
7.1452777781
 
3.3%
7.3751
 
3.3%
7.5055555561
 
3.3%
7.3508333331
 
3.3%
7.9316666671
 
3.3%
7.2838888891
 
3.3%
7.6605555561
 
3.3%
7.7391666671
 
3.3%
Other values (20)20
66.7%
ValueCountFrequency (%)
6.8847222221
3.3%
6.9416666671
3.3%
6.981
3.3%
7.1047222221
3.3%
7.1197222221
3.3%
7.1294444441
3.3%
7.1452777781
3.3%
7.1691666671
3.3%
7.1783333331
3.3%
7.2488888891
3.3%
ValueCountFrequency (%)
8.3766666671
3.3%
8.2702777781
3.3%
8.1552777781
3.3%
8.0686111111
3.3%
7.9316666671
3.3%
7.7597222221
3.3%
7.7391666671
3.3%
7.6605555561
3.3%
7.5908333331
3.3%
7.5766666671
3.3%

SS_Midpoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.4580463
Minimum14.28777778
Maximum16.95583333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:14:59.487461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum14.28777778
5-th percentile14.71609722
Q114.89743055
median15.44152778
Q315.80534722
95-th percentile16.45265278
Maximum16.95583333
Range2.668055556
Interquartile range (IQR)0.9079166689

Descriptive statistics

Standard deviation0.627717034
Coefficient of variation (CV)0.04060778587
Kurtosis-0.2206116081
Mean15.4580463
Median Absolute Deviation (MAD)0.4230555553
Skewness0.4043874247
Sum463.7413889
Variance0.3940286747
MonotonicityNot monotonic
2022-11-23T08:14:59.742428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14.849722221
 
3.3%
14.775833331
 
3.3%
15.491111111
 
3.3%
15.808333341
 
3.3%
15.088888891
 
3.3%
15.313333331
 
3.3%
16.30251
 
3.3%
15.796388891
 
3.3%
16.077222221
 
3.3%
15.693333331
 
3.3%
Other values (20)20
66.7%
ValueCountFrequency (%)
14.287777781
3.3%
14.692222221
3.3%
14.745277781
3.3%
14.775833331
3.3%
14.790555561
3.3%
14.795555551
3.3%
14.809722221
3.3%
14.849722221
3.3%
15.040555561
3.3%
15.088888891
3.3%
ValueCountFrequency (%)
16.955833331
3.3%
16.455277781
3.3%
16.449444441
3.3%
16.30251
3.3%
16.155833331
3.3%
16.077222221
3.3%
15.886666671
3.3%
15.808333341
3.3%
15.796388891
3.3%
15.693333331
3.3%

SS_SleepScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.2
Minimum59
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:00.063386image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile74.45
Q181
median85
Q390
95-th percentile91.55
Maximum94
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.140945557
Coefficient of variation (CV)0.08480932966
Kurtosis4.019184972
Mean84.2
Median Absolute Deviation (MAD)5
Skewness-1.551691978
Sum2526
Variance50.99310345
MonotonicityNot monotonic
2022-11-23T08:15:00.233569image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
904
13.3%
813
 
10.0%
913
 
10.0%
792
 
6.7%
852
 
6.7%
882
 
6.7%
842
 
6.7%
921
 
3.3%
591
 
3.3%
771
 
3.3%
Other values (9)9
30.0%
ValueCountFrequency (%)
591
 
3.3%
741
 
3.3%
751
 
3.3%
771
 
3.3%
792
6.7%
801
 
3.3%
813
10.0%
821
 
3.3%
831
 
3.3%
842
6.7%
ValueCountFrequency (%)
941
 
3.3%
921
 
3.3%
913
10.0%
904
13.3%
891
 
3.3%
882
6.7%
871
 
3.3%
861
 
3.3%
852
6.7%
842
6.7%

SS_DeepSleepDuration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7333333
Minimum73
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:00.411564image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile79.45
Q1112.75
median125
Q3144.25
95-th percentile169.5
Maximum181
Range108
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation26.30973772
Coefficient of variation (CV)0.2075991929
Kurtosis0.02383058561
Mean126.7333333
Median Absolute Deviation (MAD)15
Skewness-0.03476025819
Sum3802
Variance692.2022989
MonotonicityNot monotonic
2022-11-23T08:15:00.602566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1112
 
6.7%
1262
 
6.7%
1182
 
6.7%
1472
 
6.7%
1642
 
6.7%
1121
 
3.3%
731
 
3.3%
791
 
3.3%
1051
 
3.3%
1241
 
3.3%
Other values (15)15
50.0%
ValueCountFrequency (%)
731
3.3%
791
3.3%
801
3.3%
951
3.3%
1051
3.3%
1112
6.7%
1121
3.3%
1151
3.3%
1161
3.3%
1182
6.7%
ValueCountFrequency (%)
1811
3.3%
1741
3.3%
1642
6.7%
1472
6.7%
1461
3.3%
1451
3.3%
1421
3.3%
1411
3.3%
1371
3.3%
1321
3.3%

SS_REMSleepDuration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.13333333
Minimum26
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:00.778531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29.35
Q155.25
median75.5
Q391.5
95-th percentile103.75
Maximum111
Range85
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation23.18699596
Coefficient of variation (CV)0.3170509931
Kurtosis-0.5538974107
Mean73.13333333
Median Absolute Deviation (MAD)19.5
Skewness-0.4644466584
Sum2194
Variance537.6367816
MonotonicityNot monotonic
2022-11-23T08:15:00.934003image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
823
 
10.0%
962
 
6.7%
872
 
6.7%
522
 
6.7%
752
 
6.7%
551
 
3.3%
281
 
3.3%
951
 
3.3%
681
 
3.3%
561
 
3.3%
Other values (14)14
46.7%
ValueCountFrequency (%)
261
3.3%
281
3.3%
311
3.3%
471
3.3%
491
3.3%
522
6.7%
551
3.3%
561
3.3%
601
3.3%
681
3.3%
ValueCountFrequency (%)
1111
 
3.3%
1061
 
3.3%
1011
 
3.3%
991
 
3.3%
962
6.7%
951
 
3.3%
931
 
3.3%
872
6.7%
823
10.0%
811
 
3.3%

SS_LightSleepDuration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.9
Minimum90
Maximum311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:01.109654image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile165.8
Q1192
median203
Q3233.5
95-th percentile285.2
Maximum311
Range221
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation42.93005538
Coefficient of variation (CV)0.2025958253
Kurtosis1.981896081
Mean211.9
Median Absolute Deviation (MAD)17
Skewness0.02539477446
Sum6357
Variance1842.989655
MonotonicityNot monotonic
2022-11-23T08:15:01.267760image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2202
 
6.7%
1922
 
6.7%
2612
 
6.7%
2032
 
6.7%
1932
 
6.7%
2521
 
3.3%
1961
 
3.3%
2321
 
3.3%
3111
 
3.3%
1861
 
3.3%
Other values (15)15
50.0%
ValueCountFrequency (%)
901
3.3%
1551
3.3%
1791
3.3%
1811
3.3%
1851
3.3%
1861
3.3%
1871
3.3%
1922
6.7%
1932
6.7%
1941
3.3%
ValueCountFrequency (%)
3111
3.3%
3051
3.3%
2612
6.7%
2531
3.3%
2521
3.3%
2461
3.3%
2341
3.3%
2321
3.3%
2202
6.7%
2181
3.3%

SS_TotalSleepTime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.3
Minimum258
Maximum534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:01.427760image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum258
5-th percentile351.7
Q1385.5
median415.5
Q3442
95-th percentile463.65
Maximum534
Range276
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation48.67354305
Coefficient of variation (CV)0.1180537062
Kurtosis3.130019098
Mean412.3
Median Absolute Deviation (MAD)29.5
Skewness-0.6273188149
Sum12369
Variance2369.113793
MonotonicityNot monotonic
2022-11-23T08:15:01.590278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4611
 
3.3%
4471
 
3.3%
3821
 
3.3%
3661
 
3.3%
4651
 
3.3%
4281
 
3.3%
3401
 
3.3%
3741
 
3.3%
3711
 
3.3%
4331
 
3.3%
Other values (20)20
66.7%
ValueCountFrequency (%)
2581
3.3%
3401
3.3%
3661
3.3%
3711
3.3%
3741
3.3%
3781
3.3%
3821
3.3%
3851
3.3%
3871
3.3%
3901
3.3%
ValueCountFrequency (%)
5341
3.3%
4651
3.3%
4621
3.3%
4611
3.3%
4551
3.3%
4541
3.3%
4471
3.3%
4431
3.3%
4391
3.3%
4331
3.3%

SS_TimeToSleep
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.66666667
Minimum5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:01.766220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.35
Q110.25
median17.5
Q331.75
95-th percentile50.1
Maximum79
Range74
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation16.68642507
Coefficient of variation (CV)0.7361658119
Kurtosis3.179006505
Mean22.66666667
Median Absolute Deviation (MAD)9.5
Skewness1.601224972
Sum680
Variance278.4367816
MonotonicityNot monotonic
2022-11-23T08:15:01.953984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
85
16.7%
152
 
6.7%
172
 
6.7%
182
 
6.7%
52
 
6.7%
191
 
3.3%
241
 
3.3%
161
 
3.3%
341
 
3.3%
211
 
3.3%
Other values (12)12
40.0%
ValueCountFrequency (%)
52
 
6.7%
85
16.7%
101
 
3.3%
111
 
3.3%
131
 
3.3%
152
 
6.7%
161
 
3.3%
172
 
6.7%
182
 
6.7%
191
 
3.3%
ValueCountFrequency (%)
791
3.3%
511
3.3%
491
3.3%
401
3.3%
391
3.3%
361
3.3%
341
3.3%
331
3.3%
281
3.3%
271
3.3%

SS_WASO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.7
Minimum12
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:02.127005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18.45
Q128
median37
Q344
95-th percentile81.1
Maximum104
Range92
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.6734549
Coefficient of variation (CV)0.4955530201
Kurtosis4.064914753
Mean39.7
Median Absolute Deviation (MAD)7
Skewness1.777102325
Sum1191
Variance387.0448276
MonotonicityNot monotonic
2022-11-23T08:15:02.282072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
443
 
10.0%
502
 
6.7%
382
 
6.7%
362
 
6.7%
432
 
6.7%
332
 
6.7%
312
 
6.7%
351
 
3.3%
261
 
3.3%
121
 
3.3%
Other values (12)12
40.0%
ValueCountFrequency (%)
121
3.3%
181
3.3%
191
3.3%
211
3.3%
221
3.3%
241
3.3%
261
3.3%
271
3.3%
312
6.7%
332
6.7%
ValueCountFrequency (%)
1041
 
3.3%
911
 
3.3%
691
 
3.3%
502
6.7%
491
 
3.3%
443
10.0%
432
6.7%
411
 
3.3%
391
 
3.3%
382
6.7%

SS_CaffeineCups
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1333333333
Minimum0
Maximum1
Zeros26
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:02.427091image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3457459036
Coefficient of variation (CV)2.593094277
Kurtosis3.385989011
Mean0.1333333333
Median Absolute Deviation (MAD)0
Skewness2.272519435
Sum4
Variance0.1195402299
MonotonicityNot monotonic
2022-11-23T08:15:02.572281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
026
86.7%
14
 
13.3%
ValueCountFrequency (%)
026
86.7%
14
 
13.3%
ValueCountFrequency (%)
14
 
13.3%
026
86.7%

SS_AlcoholDrinks
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2333333333
Minimum0
Maximum3
Zeros26
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:02.709368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.55
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6789105539
Coefficient of variation (CV)2.90961666
Kurtosis10.45987632
Mean0.2333333333
Median Absolute Deviation (MAD)0
Skewness3.218761507
Sum7
Variance0.4609195402
MonotonicityNot monotonic
2022-11-23T08:15:02.839370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
026
86.7%
12
 
6.7%
31
 
3.3%
21
 
3.3%
ValueCountFrequency (%)
026
86.7%
12
 
6.7%
21
 
3.3%
31
 
3.3%
ValueCountFrequency (%)
31
 
3.3%
21
 
3.3%
12
 
6.7%
026
86.7%

SS_Stress
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9
Minimum0
Maximum21
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:03.030104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q14
median6
Q39
95-th percentile14.65
Maximum21
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.714979797
Coefficient of variation (CV)0.6833304053
Kurtosis1.5555498
Mean6.9
Median Absolute Deviation (MAD)2.5
Skewness1.04629462
Sum207
Variance22.23103448
MonotonicityNot monotonic
2022-11-23T08:15:03.174923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
65
16.7%
45
16.7%
132
 
6.7%
02
 
6.7%
82
 
6.7%
72
 
6.7%
112
 
6.7%
32
 
6.7%
92
 
6.7%
161
 
3.3%
Other values (5)5
16.7%
ValueCountFrequency (%)
02
 
6.7%
11
 
3.3%
21
 
3.3%
32
 
6.7%
45
16.7%
51
 
3.3%
65
16.7%
72
 
6.7%
82
 
6.7%
92
 
6.7%
ValueCountFrequency (%)
211
 
3.3%
161
 
3.3%
132
 
6.7%
112
 
6.7%
101
 
3.3%
92
 
6.7%
82
 
6.7%
72
 
6.7%
65
16.7%
51
 
3.3%

SS_Sleepiness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.96666667
Minimum5
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-23T08:15:03.324996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.45
Q110
median12.5
Q315
95-th percentile20.65
Maximum22
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.114594155
Coefficient of variation (CV)0.317320886
Kurtosis0.02564256894
Mean12.96666667
Median Absolute Deviation (MAD)2.5
Skewness0.4742083825
Sum389
Variance16.92988506
MonotonicityNot monotonic
2022-11-23T08:15:03.489147image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
115
16.7%
145
16.7%
104
13.3%
92
 
6.7%
152
 
6.7%
172
 
6.7%
222
 
6.7%
81
 
3.3%
161
 
3.3%
121
 
3.3%
Other values (5)5
16.7%
ValueCountFrequency (%)
51
 
3.3%
71
 
3.3%
81
 
3.3%
92
 
6.7%
104
13.3%
115
16.7%
121
 
3.3%
131
 
3.3%
145
16.7%
152
 
6.7%
ValueCountFrequency (%)
222
 
6.7%
191
 
3.3%
181
 
3.3%
172
 
6.7%
161
 
3.3%
152
 
6.7%
145
16.7%
131
 
3.3%
121
 
3.3%
115
16.7%

SS_Mood
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)8.7%
Missing7
Missing (%)23.3%
Memory size282.0 B
Good
13 
Bad
10 

Length

Max length4
Median length4
Mean length3.565217391
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowBad
3rd rowBad
4th rowBad
5th rowBad

Common Values

ValueCountFrequency (%)
Good13
43.3%
Bad10
33.3%
(Missing)7
23.3%

Length

2022-11-23T08:15:03.663758image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-23T08:15:03.772721image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good13
56.5%
bad10
43.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Location
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size282.0 B
College
24 
Home

Length

Max length7
Median length7
Mean length6.4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowCollege
4th rowCollege
5th rowCollege

Common Values

ValueCountFrequency (%)
College24
80.0%
Home6
 
20.0%

Length

2022-11-23T08:15:03.894722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-23T08:15:04.010723image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
college24
80.0%
home6
 
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-11-23T08:14:53.807809image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:23.422418image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:25.948325image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.091187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:30.585917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:32.942036image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:35.406299image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:37.853652image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:39.961334image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:42.183507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.037839image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.066262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:49.372279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:51.635464image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:54.086829image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:23.584425image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.080292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.235227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:30.761951image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.135780image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:35.527853image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:37.996220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.195347image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:42.405506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.160800image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.198500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:49.506472image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:51.765012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:54.748401image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:23.736809image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.255005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.402185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:30.926918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.285103image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:35.674852image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.147909image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.365392image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:42.596509image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.343995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.358266image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:49.671520image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:51.951984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:54.914971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:23.892776image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.416494image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.543216image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:31.118918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.435071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:35.811854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.284951image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.516395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:42.741542image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.493019image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.513267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:49.825167image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:52.145953image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:55.087056image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:24.561968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.578498image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.708181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:31.329975image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.588103image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:36.188861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.534121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.675397image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:42.903542image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.684589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.663265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:49.986393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:52.317986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:55.232181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:24.694998image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.721529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:28.858181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:31.490944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.739004image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:36.355888image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.702065image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.821392image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:43.057508image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.829984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.805298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:50.137293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:52.454976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:55.369029image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:24.812966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.856529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:29.004241image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:31.626987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:33.904008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:36.473886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.825109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:40.948429image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:43.193542image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:45.961626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:47.963326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:50.323549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:52.592983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:55.511446image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:24.937971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:26.992119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:29.138196image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:31.767970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:34.098365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:36.605856image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:38.954095image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-23T08:14:41.087427image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

2022-11-23T08:15:04.121722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T08:15:04.424757image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T08:15:04.741771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T08:15:05.033072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-23T08:15:05.228519image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T08:14:56.812594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T08:14:57.537592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-23T08:14:57.821634image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateSS_BedTimeSS_wakeTimeSS_MidpointSS_SleepScoreSS_DeepSleepDurationSS_REMSleepDurationSS_LightSleepDurationSS_TotalSleepTimeSS_TimeToSleepSS_WASOSS_CaffeineCupsSS_AlcoholDrinksSS_StressSS_SleepinessSS_MoodLocation
02022-10-0122.3872227.31222214.849722921129625246119500.00.06.011.0NaNHome
12022-10-0222.5716676.98000014.775833919510624644718310.00.013.014.0NaNHome
22022-10-0322.7347226.88472214.809722791263125341151220.00.06.08.0NaNCollege
32022-10-0424.5075007.26583315.88666785118741853788180.01.04.011.0NaNCollege
42022-10-0522.2797227.10472214.692222811168722042315910.00.013.010.0NaNCollege
52022-10-0823.1347227.75972215.447222901419620644333381.00.06.010.0NaNHome
62022-10-0923.7241677.59083315.657500881478119242113360.00.016.016.0NaNCollege
72022-10-1122.2988897.28222214.790556841742626146239360.00.00.012.0GoodCollege
82022-10-1222.1786117.31194414.745278821154926142549691.00.021.05.0BadCollege
92022-10-1322.3372227.25388914.795556901817619845540390.00.04.09.0BadCollege

Last rows

DateSS_BedTimeSS_wakeTimeSS_MidpointSS_SleepScoreSS_DeepSleepDurationSS_REMSleepDurationSS_LightSleepDurationSS_TotalSleepTimeSS_TimeToSleepSS_WASOSS_CaffeineCupsSS_AlcoholDrinksSS_StressSS_SleepinessSS_MoodLocation
202022-10-3123.6561117.45611115.556111861646818141411381.00.06.015.0GoodCollege
212022-11-0223.6475007.73916715.69333391130822204338440.00.011.014.0GoodCollege
222022-11-0324.4938897.66055616.07722279124521943718501.00.07.011.0BadCollege
232022-11-0424.3088897.28388915.79638983105751933745330.02.03.019.0GoodCollege
242022-11-0524.6733337.93166716.30250075798217934079120.00.09.017.0BadHome
252022-11-0623.2758337.35083315.313333901479518642810430.00.09.014.0BadCollege
262022-11-0722.6722227.50555615.088889851262831146521440.00.04.010.0GoodCollege
272022-11-0824.2416677.37500015.80833377736023236634260.00.08.022.0GoodCollege
282022-11-0923.8369447.14527815.491111811425218738216350.00.01.022.0GoodCollege
292022-11-1023.1102788.06861115.589444891648720345427490.01.04.011.0BadCollege